Influence of Phosphorus Sources on the Compressive Strength and Microstructure of Ferronickel Slag-Based Magnesium Phosphate Cement

Electric furnace ferronickel slag (EFS) is a typical magnesium-rich industrial by-product discharged from the manufacture of nickel and iron-nickel alloys. The approach to use it as the raw material for the preparation of magnesium phosphate cement (MPC) has potential and proves effec-tive. In this study, three different phosphorus sources (PS) including phosphoric acid (H3PO4, PA), sodium dihydrogen phosphate (NaH2 PO4, SDP) and potassium dihydrogen phosphate (KH2 PO4, PDP) were used to react with EFS to prepare the EFS-based MPC (EMPC), and the effects of raw material mass ratio (EFS/PA, EFS/SDP, EFS/PDP) on the compressive strength, early hydration temperature and microstructure of EMPC pastes were investigated. Results showed that the compressive strength of EMPC paste is significantly impacted by the type of phosphorus source and the raw materials mass ratio. When the EFS/PDP ratio is 4.0, the compressive strength of the MPC paste reaches up to 18.8, 22.8 and 27.5 MPa at 3, 7 and 28 d, respectively. Cattiite (Mg3(PO4 )2·22H2 O), K-struvite (KMgPO4·6H2O) and/or Na-struvite (NaMgPO4·6H2O) were identified as the main hydration products of EMPC. The development of EMPC mainly involves the dissolution of a phosphorus source, MgO and Mg2SiO4, formation of hydration product as binder, and combination of the unreacted raw materials together by binders to build a compact form

Upcycling Steel Slag in Producing Eco-Efficient Iron–calcium Phosphate Cement

In the present study, steel slag powder (SSP) was utilized as the raw material to prepare iron-calcium phosphate cement (ICPC) by reacting with ammonium dihydrogen phosphate (ADP). The influences of the raw materials (SSP/ADP) mass ratios ranging from 2.0 to 7.0 on the properties and microstructures of ICPC pastes were investigated. The compressive strengths of ICPC pastes at all ages firstly increased and then decreased with the increase of SSP/ADP, and the SSP/ADP of 6.0 gave the highest strength. Crystalline mundrabillaite and amorphous phases [i.e. Fe(OH)3, Al(OH)3 and H4SiO4] were formed as the dominant binding phases through the reactions of the calcium-containing compounds (brownmillerite, monticellite and srebrodolskite) in the steel slag and ADP. Further, ADP could also react with the free FeO contained in the steel slag to yield amorphous iron phosphate phase. BSE analysis indicated that the hydration products formed and growed on the surface of steel slag particles and connect them to form the continuous, dense microstructure of ICPC paste. The utilization of high-volume steel slag as the base component will potentially bring great economic and environmental benefits for the manufacture of phosphate cement

Influence of Phosphorus Sources on the Compressive Strength and Microstructure of Ferronickel Slag-Based Magnesium Phosphate Cement

Electric furnace ferronickel slag (EFS) is a typical magnesium-rich industrial by-product discharged from the manufacture of nickel and iron-nickel alloys. The approach to use it as the raw material for the preparation of magnesium phosphate cement (MPC) has potential and proves effec-tive. In this study, three different phosphorus sources (PS) including phosphoric acid (H3 PO4, PA), sodium dihydrogen phosphate (NaH2 PO4, SDP) and potassium dihydrogen phosphate (KH2 PO4, PDP) were used to react with EFS to prepare the EFS-based MPC (EMPC), and the effects of raw material mass ratio (EFS/PA, EFS/SDP, EFS/PDP) on the compressive strength, early hydration temperature and microstructure of EMPC pastes were investigated. Results showed that the compressive strength of EMPC paste is significantly impacted by the type of phosphorus source and the raw materials mass ratio. When the EFS/PDP ratio is 4.0, the compressive strength of the MPC paste reaches up to 18.8, 22.8 and 27.5 MPa at 3, 7 and 28 d, respectively. Cattiite (Mg3 (PO4 )2·22H2 O), K-struvite (KMgPO4·6H2 O) and/or Na-struvite (NaMgPO4·6H2 O) were identified as the main hydration prod-ucts of EMPC. The development of EMPC mainly involves the dissolution of a phosphorus source, MgO and Mg2 SiO4, formation of hydration product as binder, and combination of the unreacted raw materials together by binders to build a compact form

Fault identification technology for gear tooth surface wear based on MPE method by MI and improved FNN algorithm

Multiscale Permutation Entropy (MPE) is a presented nonlinear dynamic technology for measuring the randomness and detecting the nonlinear dynamic change of time sequences and can be used effectively to extract the nonlinear dynamic wear fault feature of gear tooth surface from vibration signals of gear set. To solve the subjectivity drawback of threshold parameter selection process in MPE method, a joint calculation method based on the Mutual Information (MI) and improved False Nearest Neighbor (FNN) principle for calculating threshold parameters for MPE method was presented in this article. Then, the influence of threshold parameters on the identification accuracy of fault features with the MPE was studied by analyzing simulation data. Through the simulation analysis, the effectiveness of the proposed MPE method is validated. Finally, the wear failure test of spur gear was carried out, and the proposed method was applied to analyze the experimental data of fault signal. Meanwhile, the vibration characteristics of the fault signal are acquired. The analysis results show that the proposed method can effectively realize the fault diagnosis of gear box and has higher fault identification accuracy than the existing methods

Influence of characteristic parameters of signal on fault feature extraction of singular value method

The detection of mechanical fault signals by singular value decomposition is a commonly used method in fault diagnosis. The delay time of the fault signal time series and the rationality of the value of the phase space embedding dimension, as well as the fluctuation of the characteristic parameters of the fault signal, will cause the singular value decomposition method to have a greater impact on the accuracy of fault feature identification and diagnosis. In this article, the simulation model of the similarity signal is established by the combination of the autocorrelation function method and the Cao’s algorithm. Then, the delay time of the signal sequence and the optimal value of the embedded dimension are obtained through simulation. Next, using this method to study the fluctuation of the characteristic parameters such as the frequency, amplitude and initial phase of the signal, the relationship between the characteristic parameters of the signal and the singular value of the signal is obtained. Finally, through the experimental study of the pitting corrosion of the gear tooth surface, the vibration of the fault feature is obtained. The research shows that the combination of autocorrelation function method and Cao's algorithm can calculate the optimal characteristic parameters for the singular value decomposition method and improve the ability of the method to identify fault features

A Novel Iron Phosphate Cement Derived from Copper Smelting Slag and its Early Age Hydration Mechanism

Copper slag (CS), a by-product of copper smelting, is normally stockpiled, leading to wastes of resource and space as well as environment pollution. It has not been massively reutilized as a supplementary cementitious material in Portland cement due to its low reactivity. In the present study, CS is for the first time utilized as the base component to prepare an iron phosphate cement (IPC) by reacting with ammonium dihydrogen phosphate (ADP) at room temperature. The influence of the raw materials mass ratio (CS/ADP) on the microstructure and performance of IPC pastes are investigated. It is found that the compressive strength of IPC pastes at all ages is not a monotonic function of CS/ADP, and the paste with CS/ADP of 2.0 gives the highest strengths, i.e., 26.8, 38.9 and 47.5 MPa at 1, 3 and 28 d, respectively. The crystalline phases including FeH2P3O10·H2O and FePO4 are formed as the main reaction products to bind the unreacted CS particles. The early age hydration of IPC is found to be a multi-stage process, involving the initial dissolution of ADP and iron-containing phases of CS, the formation of FeH2P3O10·H2O, the initial generation of FePO4, and the attainment of the hydration reaction equilibrium. Unlike the magnesium phosphate cement, a redox reaction of Fe(Ⅱ) into Fe(Ⅲ) occurs due to the suitable range of pH and oxidation-reduction potential of the IPC system during the hydration reaction

Unifying Image Processing as Visual Prompting Question Answering

Image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally, task-specific models are developed for individual tasks and designing such models requires distinct expertise. Building upon the success of large language models (LLMs) in natural language processing (NLP), there is a similar trend in computer vision, which focuses on developing large-scale models through pretraining and in-context learning. This paradigm shift reduces the reliance on task-specific models, yielding a powerful unified model to deal with various tasks. However, these advances have predominantly concentrated on high-level vision tasks, with less attention paid to low-level vision tasks. To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc. Our proposed framework, named PromptGIP, unifies these diverse image processing tasks within a universal framework. Inspired by NLP question answering (QA) techniques, we employ a visual prompting question answering paradigm. Specifically, we treat the input-output image pair as a structured question-answer sentence, thereby reprogramming the image processing task as a prompting QA problem. PromptGIP can undertake diverse cross-domain tasks using provided visual prompts, eliminating the need for task-specific finetuning. Our methodology offers a universal and adaptive solution to general image processing. While PromptGIP has demonstrated a certain degree of out-of-domain task generalization capability, further research is expected to fully explore its more powerful emergent generalization.Comment: 16 pages, 12 figure

HAT: Hybrid Attention Transformer for Image Restoration

Transformer-based methods have shown impressive performance in image restoration tasks, such as image super-resolution and denoising. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better restoration, we propose a new Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to further exploit the potential of the model for further improvement. Extensive experiments have demonstrated the effectiveness of the proposed modules. We further scale up the model to show that the performance of the SR task can be greatly improved. Besides, we extend HAT to more image restoration applications, including real-world image super-resolution, Gaussian image denoising and image compression artifacts reduction. Experiments on benchmark and real-world datasets demonstrate that our HAT achieves state-of-the-art performance both quantitatively and qualitatively. Codes and models are publicly available at https://github.com/XPixelGroup/HAT.Comment: Extended version of HA

Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video

Real-time eyeblink detection in the wild can widely serve for fatigue detection, face anti-spoofing, emotion analysis, etc. The existing research efforts generally focus on single-person cases towards trimmed video. However, multi-person scenario within untrimmed videos is also important for practical applications, which has not been well concerned yet. To address this, we shed light on this research field for the first time with essential contributions on dataset, theory, and practices. In particular, a large-scale dataset termed MPEblink that involves 686 untrimmed videos with 8748 eyeblink events is proposed under multi-person conditions. The samples are captured from unconstrained films to reveal "in the wild" characteristics. Meanwhile, a real-time multi-person eyeblink detection method is also proposed. Being different from the existing counterparts, our proposition runs in a one-stage spatio-temporal way with end-to-end learning capacity. Specifically, it simultaneously addresses the sub-tasks of face detection, face tracking, and human instance-level eyeblink detection. This paradigm holds 2 main advantages: (1) eyeblink features can be facilitated via the face's global context (e.g., head pose and illumination condition) with joint optimization and interaction, and (2) addressing these sub-tasks in parallel instead of sequential manner can save time remarkably to meet the real-time running requirement. Experiments on MPEblink verify the essential challenges of real-time multi-person eyeblink detection in the wild for untrimmed video. Our method also outperforms existing approaches by large margins and with a high inference speed.Comment: Accepted by CVPR 202